Analysts in various professions may, at times, be called upon to search relatively large collections of imagery to identify, if present, various types of relevant information (referred to herein as “a target entity” or “target entities”) in the collection of imagery. For example, medical analysts sometimes diagnose a physical impairment by searching complex imagery collections to identify one or more target entities therein that may be the cause of the physical impairment. Moreover, intelligence analysts may be called upon to search relatively complex imagery collections to identify target entities therein that may relate to various types of intelligence gathering activities.
Advancements in both image collection and storage technology presently allow for the relatively low-cost storage of large volumes of high-quality imagery. However, the cost of searching through large sets of imagery for target entities can often be substantial. Indeed, in many professions, such as intelligence gathering, effective searching may rely on the expertise of highly skilled analysts, who typically search through relatively large sequences of images in a relatively slow manner. Presently, the number of skilled analysts available to search the amount of imagery that is stored, or can potentially be stored, is in many instances insufficient.
In response to the foregoing, there has relatively recently been a focus on developing various systems and methods for triaging imagery. One of the methods that has shown promise combines electroencephalography (EEG) technology and rapid serial visualization presentation (RSVP). Various implementations of this combination have been researched and developed. For example, researchers have experimented with a system in which users are presented, using the RSVP paradigm, a sequence of images, some of which may include particular types of target entities. During the RSVP presentation, EEG data and/or physical response data are collected from the users. A trainable classifier processes the collected EEG data and/or physical response data to assign probabilities to each image. The probabilities are representative of the likelihood an image includes a target.
Although useful in sorting a sequence of images, the above described system and method, as well as other systems and methods that employ these same technologies, do suffer certain drawbacks. For example, prior to the performance phase, in which images are searched for target entities, present systems and methods typically implement a calibration phase. During the calibration phase, images with known target entities are displayed to a user, and these data are used to train (or calibrate) the classifier. Present systems and methods thus rely on the calibration data collected during the calibration phase, even though signal characteristics during the performance phase may have changed since completion of the calibration phase. In particular, the characteristics of both the neural signals and/or the physical response signals change over time. As a result, the classifier may not be as accurate during later portions of the performance phase, which may lead to degraded target detection performance.
Hence, there is a need for an efficient and effective system and method for increasing the likelihood of target identification in images after an initial calibration phase and throughout a performance phase. The present invention addresses at least this need.